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CL-Informer: Long time series prediction model based on continuous wavelet transform

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  • Baijin Liu
  • Zimei Li
  • Zhanlin Li
  • Cheng Chen

Abstract

Time series, a type of data that measures how things change over time, remains challenging to predict. In order to improve the accuracy of time series prediction, a deep learning model CL-Informer is proposed. In the Informer model, an embedding layer based on continuous wavelet transform is added so that the model can capture the characteristics of multi-scale data, and the LSTM layer is used to capture the data dependency further and process the redundant information in continuous wavelet transform. To demonstrate the reliability of the proposed CL-Informer model, it is compared with mainstream forecasting models such as Informer, Informer+, and Reformer on five datasets. Experimental results demonstrate that the CL-Informer model achieves an average reduction of 30.64% in MSE across various univariate prediction horizons and a reduction of 10.70% in MSE across different multivariate prediction horizons, thereby improving the accuracy of Informer in long sequence prediction and enhancing the model’s precision.

Suggested Citation

  • Baijin Liu & Zimei Li & Zhanlin Li & Cheng Chen, 2024. "CL-Informer: Long time series prediction model based on continuous wavelet transform," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0303990
    DOI: 10.1371/journal.pone.0303990
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    References listed on IDEAS

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    1. Ivan Svetunkov & John E. Boylan, 2020. "State-space ARIMA for supply-chain forecasting," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 818-827, February.
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